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Improving passenger travel efficiency through a dynamic autonomous non-stop rail transit system

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  • Wu, Pei-Yang
  • Guo, Ren-Yong
  • Lin, Zhiyuan
  • Liu, Ronghui
  • Xu, Pu

Abstract

This study proposes a dynamic autonomous non-stop rail transit (DANRT) system to improve the travel efficiency of passengers in urban rail transit (URT) systems and solves a pertinent carriage scheduling problem derived from the DANRT system using a mathematical programming model. In the DANRT system, passengers traveling to the same destination are allocated to the same carriage(s), and each carriage can be attached to and detached from trains using the modular autonomous vehicle (MAV) technology effortlessly, which enables all trains to run non-stop throughout the focused operation period. We offer a cost-effective design for the DANRT system. To ensure safe and efficient operations, a mathematical model is proposed for the carriage scheduling problem in the DANRT system, where the number and destinations of carriages required by each station are determined. A linearization and segmentation method for the model is proposed. To examine the effectiveness of the DANRT system, we compare the travel efficiency of passengers in the DANRT system with that in the traditional system by using the origin-destination distribution of passengers on the Batong Line of Beijing Subway. The results demonstrate that passengers in DANRT can save about 2.9 % to 8.6 % of travel time compared with the traditional system. Finally, we conclude several observations and operational characteristics of the DANRT system by numerical experiments.

Suggested Citation

  • Wu, Pei-Yang & Guo, Ren-Yong & Lin, Zhiyuan & Liu, Ronghui & Xu, Pu, 2024. "Improving passenger travel efficiency through a dynamic autonomous non-stop rail transit system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 182(C).
    • Handle: RePEc:eee:transe:v:182:y:2024:i:c:s1366554524000048
    DOI: 10.1016/j.tre.2024.103414
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    References listed on IDEAS

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